Influence Quantities

Prices of other related goods also influence quantity supplied. For example, uranium production may produce vanadium as a byproduct. Thus, uranium and... 

The sign before each variable indicates how the variable influences quantity supplied. A minus sign indicates they are inversely related and a positive sign indicates they are directly related. Thus, in the example of coal, raising the price of coal is likely to increase quantity supplied, whereas raising the price of labor is likely to decrease the quantity supplied. 

Many factors influence quantity and type of inorganic constituents in fruits. Some of the significant factors are maturity, variety, season, location, production practices, and climatic conditions (170). Since these factors are of critical importance to plant nutrition, growth, development,... 

According to the number n of included independent variables xb x (influencing quantities, features, factors,. ..) we distinguish one-factorial designs (n = 1) and multifactorial designs (n > 1). According to the number m of recorded response features v i,. .., ym we will get univariate results (m = 1) or multivariate results (m > 1). Therefore we can arrange our numerical results in data matrices with k lines, the experiments, and m... 

Demonstrating, by validation, that the calculation and measurement conditions include all of the influence quantities that significantly affect the result, or the value assigned to a standard... 

Identifying the relative importance of each influence quantity - dictated by their quantitative effect on measurement results - in order to decide on the degree of control or calibration... 

An analysis method based on a microwave-supported leaching using aqua regia was elaborated in order to determine the maximum acid-soluble proportion of nickel in sediment samples. During the development of the method, the first two activities mentioned above (compilation of significant influence quantities, and of their relative importance) were accomplished. 

In identifying the concept of traceability most closely with the process of calibration and control for known influence quantities, we risk presenting an incomplete picture of the problem of obtaining reliable results. 

Finally, it should also be pointed out that in heat conduction problems the dimensionless representation and the combination of the influencing quantities into dimensionless numbers are not as significant as in the representation and determination of heat transfer coefficients in 1.1.4. In the following sections we will frequently refrain from making the heat conduction problem dimensionless and will only present the solution of a problem in a dimensionless form by a suitable combination of variables and influencing quantities. 

Under these preconditions the physical process can be described by the following influencing quantities ... 

The cause of this is not only that there are many influencing quantities that play a role in boiling processes, but also different types of heat transfer depending on the flow configuration and superheating. These different types of heat transfer will be considered first, followed by an explanation of the physical fundamentals of boiling phenomena. The final part of this section will consist of the calculation of the heat transfer. 

Fig. 5.48 shows the extraterrestrial spectrum Ex°n and the associated pattern of EXn. The upper edge of this curve represents the irradiance reduced purely by Rayleigh scattering. The deteriorations marked in black are caused by the absorption by the gases 03, 02, H20 and C02. Further diagrams of this type, which show the variation in the influencing quantities (water vapour and ozone content, turbidness due to aerosols, different optical masses), are available in M. Iqbal [5.34]. 

Random error arises from unpredictable variations of influence quantities. These random effects give rise to variations in repeated observations of the measurand. 

The work of Robinson and Wilke [469] was probably the first one which quantitatively investigated the effect of salts on gassing of liquids. The ionic strength was established as an influence quantity, which was calculated as follows I = 0.5 52 2 Wi [g-ion/1] all ions have therefore to be taken into consideration, (zj is the charge number of an ion i and m is its molality.)... 

Influence factor (quantity) Something that may affect a measurement result. For example, temperature, pressure, solvent, analyst. In calibration, influence quantities refer to quantities that are not the independent variable but that may affect the measurement. (Sections 4.2, 4.3, 5.3)... 

Before an analysis is performed using a particular method and instrument, there must be a measurement model, an equation that relates the quantities that are to be measured to the indication of the instrument and other influence quantities such as temperature and pressure. From this model we derive the calibration equation. Usually, the experiment is performed in such a way as to fix influence quantities and the calibration equation is determined in terms of the concentration of the measurand and some output of the instrument, which might be peak height, peak area, potential, current, absorbance, etc. A simple linear relation is... 

CGTase from B. clausii strain El 6 was specific for P-CD formation displaying a P-CGTase action. The distributions of a-, P-, and y-CDs were 0, 89, and 11%, respectively, on soluble starch. The starches from different botanical sources influenced quantities and types of CD formed. It was also observed that root and tuber starches were more accessible to CGTase action. The process of starch homogenization can interfere whit the CGTase action and, consequently, on the CD formation. The gelatinization of starches by the autoclave process improves the CD production, mainly for cereal starches. 

For hydrogenation in suspension, the following seven influencing quantities are of importance temperature, pressure (H2 partial pressure), type and quantity of catalyst, starting material concentration, stirring speed, and solvent. 

However, this grouping does not influence the uncertainty evaluatimi it is just essential that a standard uncertainty is attributed to any influencing quantity X. ... 

An important feature of this approach is that only the standards that realize the unit of length require a documentary chain of evidence that links to the definition of the meter. In a typical dimensional measurement, there are many influence quantities that contribute to the uncertainty associated with the end result, such as workpiece temperature and thermal expansion coefficient. If it were required that all such influence quantities themselves be metrologically traceable, then since each of the quantities depends on other quantities and so on, such a requirement would lead to an infinite regress. 

According to GUM, a measurement has imperfections which give rise to errors in the measurement result. A random error presumably arises from unpredictable or stochastic temporal and spatial variations of influence quantities. Although it is not possible to compensate for random error, it can usually be reduced by increasing the number of observations. Systematic error, like random error, cannot be eliminated but it too can often be reduced. Once the effect causing the systematic error has been recognized, the effect can be quantifled and a correction can be applied to compensate for the effect. The uncertainty of the result of a measurement reflects the lack of exact knowledge of the value of the measurand. 

After this step, uncertainties in influence quantities must be converted to uncertainties in the analytical results. In simple cases, when measurement equations involve one algebraic operation, the following rules apply if a quantity Xi is simply added to or subtracted from others to obtain the result y, the contribution to the uncertainty in y is simply the uncertainty u Xi) in Xi. In case of a division or multiplication, the contribution of the relative uncertainty in y, u y)/y, is the relative uncertainty u xi) jxi in Xi. These mles do not apply in compHcated cases with a... 

With the help of variance analysis, it is determined how far the variance of observed variable X can be traced back to suspected influence factors. These influence factors may be qualitative or quantitative variables. Variance analysis is based on the assumption that, in addition to data of the observed variable X, data on other suspected influence factors are also present in a measuring series, whereby these influence factors can be classified in such a way that each observed value of X can be associated to a class i. In the case of a simple variance analysis with one additional influence quantity, the following equation will result for X ... 

The overall collection efficiency is affected by the selective separation of the dust according to particle size in accordance with a separation curve. For constant gas flow rate, the overall collection efficiency increases if the following influencing quantities increase settling velocity of the dust particles, agglomerating tendency of the dust, dust content of the gas to be dedusted (within limits). 

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